Interpretable XGBoost based classification of 12-lead ECGs applying information theory measures from neuroscience.
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Automated ECG classification is a standard feature in many commercial 12-Lead ECG machines. As part of the Physionet/CinC Challenge 2020, our team, "Mad-hardmax", developed an XGBoost based classification method for the analysis of 12-Lead ECGs acquired from four different countries. Our aim is to develop an interpretable classifier that outputs diagnoses which can be traced to specific ECG features, while also testing the potential of information theoretic features for ECG diagnosis. These measures capture high-level interdependencies across ECG leads which are effective for discriminating conditions with multiple complex morphologies. On unseen test data, our algorithm achieved a challenge score of 0.155 relative to a winning score of 0.533, putting our submission in 24th position from 41 successful entries.
Date Issued
2021-02-10
Date Acceptance
2021-02-01
Citation
Comput Cardiol (2010), 2021, 47, pp.1-4
ISSN
2325-8861
Publisher
IEEE
Start Page
1
End Page
4
Journal / Book Title
Comput Cardiol (2010)
Volume
47
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
British Heart Foundation
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/33763495
Grant Number
RG/16/3/32175
Source
2020 Computing in Cardiology
Publication Status
Published
Start Date
2020-09-13
Finish Date
2020-09-16
Coverage Spatial
Rimini, Italy
Date Publish Online
2021-02-10